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Model selection in linear mixed-effect models

Author

Listed:
  • Simona Buscemi

    (University of Palermo)

  • Antonella Plaia

    (University of Palermo)

Abstract

Linear mixed-effects models are a class of models widely used for analyzing different types of data: longitudinal, clustered and panel data. Many fields, in which a statistical methodology is required, involve the employment of linear mixed models, such as biology, chemistry, medicine, finance and so forth. One of the most important processes, in a statistical analysis, is given by model selection. Hence, since there are a large number of linear mixed model selection procedures available in the literature, a pressing issue is how to identify the best approach to adopt in a specific case. We outline mainly all approaches focusing on the part of the model subject to selection (fixed and/or random), the dimensionality of models and the structure of variance and covariance matrices, and also, wherever possible, the existence of an implemented application of the methodologies set out.

Suggested Citation

  • Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.
    • Handle: RePEc:spr:alstar:v:104:y:2020:i:4:d:10.1007_s10182-019-00359-z
    DOI: 10.1007/s10182-019-00359-z
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